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Retrain from scratch on dataset 4 (notebook pipeline)

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  1. README.md +33 -0
  2. model.safetensors +1 -1
  3. special_tokens_map.json +42 -6
README.md ADDED
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+ ---
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+ license: mit
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+ language: en
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+ pipeline_tag: token-classification
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+ tags:
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+ - ner
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+ - resume-parsing
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+ - cv-parser
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+ base_model: roberta-base
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+ ---
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+
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+ # CV Parser NER — roberta-base (v2)
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+
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+ Token-classification model that extracts **Job Titles**, **Skills**, and
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+ **Education** from resumes/CVs using a BIO tag scheme.
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+
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+ ## Provenance
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+ - **Trained from scratch on dataset 4** (`resume_bio_annotated_full.csv`,
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+ 2,483 resumes — 1,739 train / 372 val / 372 test), the team's finalized
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+ AI-Studio/Vertex-relabelled dataset.
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+ - Reproduced end-to-end with the project notebooks/scripts
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+ (`retokenize.py` + `train_bert_run.py`).
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+ - Base model: `roberta-base` · epochs: 5 · learning rate: 3e-5 ·
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+ max_length 512 · stride 128 · seed 42.
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+
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+ ## Resume-level performance (dataset-4 splits)
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+ | split | precision | recall | F1 |
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+ |-------|-----------|--------|----|
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+ | validation | — | — | 0.6397 |
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+ | test | — | — | 0.6563 |
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+
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+ ## Labels
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+ `O, B-JOB_TITLE, I-JOB_TITLE, B-SKILL, I-SKILL, B-EDUCATION, I-EDUCATION`
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  "mask_token": {
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  "content": "<mask>",
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  "lstrip": true,
 
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